Accuracy of artificial intelligence-assisted landmark identification in serial lateral cephalograms of Class III patients who underwent orthodontic treatment and two-jaw orthognathic surgery
홍미희, 김인환, 조인형, 강경화, 김민지, 김수정, 김윤지, 성상진, 김영호, 임성훈, 김남국, 백승학,
소속 상세정보
홍미희 ( Hong Mi-Hee ) - Kyungpook National University School of Dentistry Department of Orthodontics
김인환 ( Kim In-Hwan ) - University of Ulsan College of Medicine Asan Medical Center Department of Convergence Medicine
조인형 ( Cho In-Hyoung ) - Chonnam National University School of Dentistry Department of Orthodontics
강경화 ( Kang Kyung-Hwa ) - Wonkwang University School of Dentistry Department of Orthodontics
김민지 ( Kim Min-Ji ) - Ewha Womans University College of Medicine Department of Orthodontics
김수정 ( Kim Su-Jung ) - Kyung Hee University School of Dentistry Department of Orthodontics
김윤지 ( Kim Yoon-Ji ) - University of Ulsan College of Medicine Asan Medical Center Department of Orthodontics
성상진 ( Sung Sang-Jin ) - University of Ulsan College of Medicine Asan Medical Center Department of Orthodontics
김영호 ( Kim Young-Ho ) - Ajou University School of Medicine Department of Orthodontics
임성훈 ( Lim Sung-Hoon ) - Chosun University College of Dentistry Department of Orthodontics
김남국 ( Kim Nam-Kug ) - University of Ulsan College of Medicine Asan Medical Center Department of Convergence Medicine
백승학 ( Baek Seung-Hak ) - Seoul National University School of Dentistry Department of Orthodontics
Abstract
Objective: To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent two-jaw orthognathic surgery.
Methods: A total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed.
Results: The total mean error was 1.17 mm without significant difference among the four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups.
Conclusions: The CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.
키워드
Convolutional neural network; Landmark identification; Two-jaw orthognathic surgery; Serial lateral encephalogram
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